Welcome to the Computational Biology Lab!

Our research group develops new methods for the statistical analysis of data. The major focus of our research is guided by large-scale, high-dimensional data from genomics and genetic experiments.

Currently, we are especially interested in the analysis of gene expression data from microarray experiments on a pathways level and sequencing data. In close collaboration with groups from Biology and Medicine we are studying cancer related questions. An ultimate goal of our research is to contribute to the deciphering of regulatory networks with respect to their reconstruction and functional analysis to shed light on causal mechanisms underlying complex diseases.

Our methodological research aims to develop and improve computational and statistical methods in Bayesian statistics, exploratory data analysis, graph theory, machine learning, Monte Carlo methods, multivariate analysis, optimization and statistical inference to apply them to problems in Computational and Cancer Biology.

Research:

Computational Biology Biostatistics Machine Learning & Statistics Network Biology

For more information about our research please look here.

Selected books:

New book: Statistical diagnostics for cancer: Analyzing high-dimensional data Frank Emmert-Streib and Matthias Dehmer More information soon.	Applied Statistics for Network Biology: Methods in Systems Biology Matthias Dehmer, Frank Emmert-Streib, Armin Graber and Armindo Salvador ISBN-10: 3-527-32750-9 More information	Medical Biostatistics for Complex Diseases Frank Emmert-Streib and Matthias Dehmer ISBN: 978-3-527-32585-6 More information
Analysis of Microarray Data: A Network-Based Approach Frank Emmert-Streib and Matthias Dehmer ISBN: 978-3-527-31822-3 More information	Information Theory and Statistical Learning Frank Emmert-Streib and Matthias Dehmer ISBN: 978-0-387-84815-0 More information	Analysis of Complex Networks: From Biology to Linguistics Matthias Dehmer and Frank Emmert-Streib ISBN: 978-3-527-32345-6 More information

Selected publications:

1. F. Emmert-Streib and G. Glazko
   Network biology: a direct approach to study biological function
   Wiley Interdisciplinary Reviews: Systems Biology and Medicine (2011).
   
2. G. Altay and F. Emmert-Streib
   Inferring the conservative causal core of gene regulatory networks
   BMC Systems Biology 4:132 (2010).
   
3. F. Emmert-Streib
   Statistic complexity: combining kolmogorov complexity with an ensemble approach
   PLoS One. 5(8):e12256 (2010).
   
4. G. Altay and F. Emmert-Streib
   Revealing differences in gene network inference algorithms on the network level by ensemble methods
   Bioinformatics 26(14):1738 - 1744 (2010).
   
5. F. Emmert-Streib
   Exploratory analysis of spatiotemporal patterns of cellular automata by clustering compressibility
   Physical Review E, 81(2):026103, (2010).
   
6. G. Glazko and F. Emmert-Streib
   Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets
   Bioinformatics 25(18):2348-2354 (2009).
   
7. F. Emmert-Streib and M. Dehmer
   Information processing in the transcriptional regulatory network of yeast: Functional robustness
   BMC Systems Biology 3:35 (2009).
   
8. F. Emmert-Streib
   The Chronic Fatigue Syndrome: A Comparative Pathway Analysis
   Journal of Computational Biology, 14(7) (2007) 961-972.
   
9. L. Chen, F. Emmert-Streib and J. Storey
   Harnessing naturally randomized transcription to infer regulatory relationships among genes
   Genome Biology, 8:R219 (2007).
   
10. F. Emmert-Streib and A. Mushegian
    A Topological Algorithm for Identification of Structural Domains of Proteins
    BMC Bioinformatics, (2007) 8-237.
    
11. F. Emmert-Streib
    Algorithmic Computation of Knot Polynomials of Secondary Structure Elements of Proteins
    Journal of Computational Biology, 13(8) (2006) 1503-1512.
    

More publications can be found here.

Software:

1. c3net: Infering large-scale gene networks with the C3NET inference algorithm - R package. Available at the CRAN repository.

Contact:

Dr. Frank Emmert-Streib
 

Institute:	General Contact:
Queen's University Belfast  Computational Biology and Machine Learning Lab  Center for Cancer Research and Cell Biology  School of Medicine, Dentistry and Biomedical Sciences	Phone: +44 (0)28 9097 2792 Fax: +44-no-fax E-Mail (click here)
Postal Address:
Center for Cancer Research and Cell Biology  Queen's University Belfast  97 Lisburn Road, Belfast BT9 7BL, UK

Affiliations:

Center for Cancer Research and Cell Biology

Cancer Bioinformatics

School of Medicine, Dentistry and Biomedical Sciences

Queen's University Belfast, UK

Jobs: We are always looking for talented students and postdocs. For an inquiry please send an Email.

From the community: Funding:
We acknowledge support from the Center for Cancer Research and Cell Biology (QUB), DEL and the EPSRC.